一个基于相关性的定量膜周期骨架相关周期的工具。

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2025-08-22 eCollection Date: 2025-01-01 DOI:10.3389/fninf.2025.1628538
Sam K Vanspauwen, Virginia Luque-Fernández, Hanne B Rasmussen
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引用次数: 0

摘要

超分辨率显微镜的出现揭示了膜相关周期性骨架(MPS),这是一种特殊的神经元细胞骨架结构,由两个谱蛋白二聚体组成,间隔190 nm。虽然许多离子通道、细胞粘附分子和信号蛋白已被证明与MPS相关,但用于准确和公正地定量其周期性定位的工具仍然很少。方法:我们开发了Napari- wavebreaker (https://github.com/SamKVs/napari-k2-WaveBreaker),这是一个用于Napari图像查看器的开源插件。该工具使用自相关量化MPS周期性,并使用互相关评估目标之间的周期性共分布。使用模拟数据集和周期性和非周期性轴突蛋白的STED显微镜图像来评估性能。结果:Napari-WaveBreaker输出参数准确地反映了视觉观察到的周期性,并检测到两个周期目标之间的空间位移。该方法在不同的图像质量下具有鲁棒性,并且能够可靠地区分周期性和非周期性蛋白质分布。讨论:Napari-WaveBreaker为分析MPS相关的周期性和共分布提供了一个公正的定量框架,使人们对MPS的分子组织和调制有了新的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A correlation-based tool for quantifying membrane periodic skeleton associated periodicity.

Introduction: The advent of super-resolution microscopy revealed the membrane-associated periodic skeleton (MPS), a specialized neuronal cytoskeletal structure composed of actin rings spaced 190 nm apart by two spectrin dimers. While numerous ion channels, cell adhesion molecules, and signaling proteins have been shown to associate with the MPS, tools for accurate and unbiased quantification of their periodic localization remain scarce.

Methods: We developed Napari-WaveBreaker (https://github.com/SamKVs/napari-k2-WaveBreaker), an open-source plugin for the Napari image viewer. The tool quantifies MPS periodicity using autocorrelation and assesses periodic co-distribution between targets using cross-correlation. Performance was evaluated using both simulated datasets and STED microscopy images of periodic and non-periodic axonal proteins.

Results: Napari-WaveBreaker output parameters accurately reflected the visually observed periodicity and detected spatial shifts between two periodic targets. The approach was robust across varying image qualities and reliably distinguished periodic from non-periodic protein distributions.

Discussion: Napari-WaveBreaker provides an unbiased, quantitative framework for analyzing MPS-associated periodicity and co-distribution enabling new insights into the molecular organization and modulation of the MPS.

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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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